Bayesian Nonparametrics
This page contains resources about Bayesian Nonparametrics. Subfields See Category:Bayesian Nonparametrics for some of its subfields. * Nonparametric Empirical Bayes (NPEB) Method * Gamma Process Nonnegative Matrix Factorization * Gaussian Process * Dirichlet Process * Chinese Restaurant Process (CRP) * Chinese Restaurant Franchise (CRF) * Indian Buffet Process (IBP) * Pitman–Yor Process * Hierarchical Dirichlet Process (HDP) * Mixture of Dirichlet Processes (MDP) * Dirichlet Processes Mixture Model (DPMM) * CRP Mixture Model * IBP Latent Factor Model * Latent Dirichlet Allocation (LDA) * Lévy Process * Bernoulli Process * Completely Random Measures ** Poisson Random Measure / Poisson Point Process ** Gamma Process ** Beta Process / Beta-Bernoulli Process ** Stable Process * Pólya Trees * Pólya's Urn Process * Hoppe's Urn Process * Stick Breaking Process Online Courses Video Lectures * Bayesian Nonparametrics by Yee Whye Teh - VideoLectures.Net * Nonparametric Bayesian Models by Yee Whye Teh - VideoLectures.Net * Dirichlet Processes, Chinese Restaurant Processes, and all that - VideoLectures.Net * Foundations of Nonparametric Bayesian Models by Peter Orbanz - VideoLectures.Net Lecture Notes * Bayesian Nonparametrics by Peter Orbanz * Bayesian Nonparametrics by David M. Blei * Advanced Methods in Probabilistic Modeling BY David M. Blei * Topics in Probability: Lévy Processes by Davar Khoshnevisan * Nonparametric Bayesian Methods (Dirichlet Process Mixtures) by Jun Zhu * Nonparametric Bayesian methods (Dirichlet processes) by Kurt Miller Books and Book Chapters * Küchler, U., & Sorensen, M. (1997). Exponential families of stochastic processes. Springer Science & Business Media. * Dey, D. D., MüIler, P., & Sinha, D. (Eds.). (1998). Practical nonparametric and semiparametric Bayesian statistics (Vol. 133). Springer Science & Business Media. * Ghosh, J. K., & Ramamoorthi, R. V. (2003). Bayesian Nonparametrics.Springer Series in Statistics. Springer-Verlag, New York, 16, 37. * Görür, D. (2007). Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning. PhD Dissertation. TU Berlin. * Koller, D., & Friedman, N. (2009). "Section 19.5: Learning Models with Hidden Variables ". Probabilistic Graphical Models. MIT Press. * Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.). (2010). Bayesian Nonparametrics. Cambridge University Press. * Orbanz, P., & Teh, Y. W. (2011). Bayesian Nonparametric Models. In Encyclopedia of Machine Learning (pp. 81-89). Springer US. * Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. Chapter 25: Clustering. MIT Press. * Jordan, M. I. (2013). Hierarchical models, nested models and completely random measures. Frontiers of Statistical Decision Making and Bayesian Analysis: in Honor of James O. Berger. New York: Springer, 207-218. * Theodoridis, S. (2015). "Section 13.12: Nonparametric Bayesian Modeling". Machine Learning: A Bayesian and Optimization Perspective. Academic Press. * Müller, P., Quintana, F. A., Jara, A., & Hanson, T. (2015). Nonparametric Bayesian Data Analysis. New York: Springer. * Phadia, E. G. (2015). Prior Processes and Their Applications: Nonparametric Bayesian Estimation. Springer. * Mitra, R., & Müller, P. (Eds.). (2015). Nonparametric Bayesian Inference in Biostatistics. Springer. * Goodman, N. D., & Tenenbaum, J. B. (2016). "Chapter 12: Non-parametric models". Probabilistic Models of Cognition. ''2nd Ed. (link) Scholarly Articles ''See also NPBayes 2008 for more references. * Damien, P. (2005). Some Bayesian Nonparametric Models. Handbook of statistics, 25, 279-314. * Hanson, T. E., Branscum, A. J., & Johnson, W. O. (2005). Bayesian nonparametric modeling and data analysis: an introduction. Handbook of statistics, 25, 245-278. * Walker, S. (2005). Bayesian Nonparametric Inference. Handbook of statistics, 25, 339-371. * Sudderth, E.B. (2006). Graphical Models for Visual Object Recognition and Tracking. Ph.D. dissertation, Massachusetts Institute of Technology. * Görür, D. (2007). Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning. Ph.D. dissertation, Max Planck Institute for Biological Cybernetics. * Thibaux, R. J. (2008). Nonparametric Bayesian Models for Machine Learning. Ph.D. dissertation, Department of Statistics, University of California, Berkeley. * Frigyik, B. A., Kapila, A., & Gupta, M. R. (2010). Introduction to the dirichlet distribution and related processes. Department of Electrical Engineering, University of Washington. UWEETR-2010-0006. * Gershman, S. J., & Blei, D. M. (2012). A tutorial on Bayesian Nonparametric Models. Journal of Mathematical Psychology, 56(1), 1-12. * Ghahramani, Z. (2013). Bayesian non-parametrics and the probabilistic approach to modelling. Phil. Trans. R. Soc. A, 371, 20110553. Tutorials * Infinite Mixture Models with Nonparametric Bayes and the Dirichlet Process by Edwin Chen * Dirichlet Processes by Emin Orhan * Dirichlet Processes: A Gentle Tutorial by Khalid El-Arini * Dirichlet processes, Chinese restaurant processes and all that by Michael Jordan - NIPS 2005 * Non-parametric Bayesian Methods by Zoubin Ghahramani - UAI 2005 * Machine Learning from a Nonparametric Bayesian Point of View by Michael Jordan (Youtube) - Rutgers 2008 * An Introduction to Bayesian Nonparametric Modelling by Yee Whye Teh - Toronto 2009 * An Introduction to Bayesian Nonparametric Modelling by Yee Whye Teh - MSR 2009 * An Introduction to Bayesian Nonparametrics by Yee Whye Teh - 2009 * An Introduction to Bayesian Nonparametric Modelling by Yee Whye Teh - MLSS 2009 * Bayesian Nonparametrics by Yee Whye Teh - CIMAT 2010 * Bayesian Nonparametrics by Yee Whye Teh - KAIST 2010 * Introduction to Bayesian Nonparametrics by Yee Whye Teh - MLSS September 2011 * An Introduction to Bayesian Nonparametric Modelling by Yee Whye Teh - MLSS June 2011 * Modern Bayesian nonparametrics by P Orbanz and YW Teh (Youtube) - NIPS 2011 * Probabilistic Modelling, Machine Learning, and the Information Revolution by Zoubin Ghahramani - 2012 * Bayesian Nonparametrics: Models Based on the Dirichlet Process by Alessandro Panella - 2013 Software * Edward: A library for probabilistic modeling, inference, and criticism - Python with TensorFlow * NPBayesHMM - Nonparametric Bayesian Inference for Sequential Data in MATLAB * DPackage - R * DIRECT - R * profdpm - R * bnpy - Python (Bitbucket) * bnpy - Python (Github) * PyMC3 - Python with Theano * datamicroscopes - Python * Nonparametric Bayesian Mixture - MATLAB and C * hal/HBC Hierarchical Bayesian compiler * mj/Software.htm Adaptor grammars * The MIT-Church project * Infer.NET - Developed by Microsoft Research * OpenBUGS - Bayesian Inference Using Gibbs Sampling * DPMM - MATLAB. Sampling (MCMC) and variational. * Variational Gaussian DPMM - MATLAB * DPVC - MATLAB * IDP - MATLAB and R * BNPgraph - MATLAB See also * Discrete Latent Variable Models * Stochastic Processes and Random Fields * Kernel Methods Other Resources * Tutorials on Bayesian Nonparametrics * Dirichlet Process: Practical course - MATLAB * notes-on-dirichlet-processes - IPython notebooks explaining DPs, HDPs, and LDA * Clustering with Dirichlet process mixtures - MATLAB practical * List of papers on Nonparametric Bayes by Yee Whye Teh * List of papers on Bayesian Nonparametrics by Michael Jordan * List of papers in Bayesian Nonparametric by Dan Roy * NPBayes 2008 - Nonparametric Bayes Workshop at ICML/UAI/COLT 2008 * NPBayes 2009 * Course notes on Bayesian Nonparametrics by Athanasios Kottas * Bayesian machine learning - Metacademy * Dirichlet Process, Infinite Mixture Models, and Clustering - Python and R * Introduction to Bayesian Nonparametrics - blog post Category:Bayesian Nonparametrics Category:Probabilistic Graphical Models